Optimization Assessment of Projection Methods of Climate Change for Discrepancies between North and South China

Yurui Lun,Liu Liu,Ruotong Wang,Guanhua Huang

doi:10.3390/w12113106

Abstract

Downscaling methods have been widely used due to the coarse and biased outputs of general circulation models (GCMs), which cannot be applied directly in regional climate change projection. Hence, appropriate selection of GCMs and downscaling methods is important for assessing the impacts of climate change. To explicitly explore the influences of multi-GCMs and different downscaling methods on climate change projection in various climate zones, the Heihe River Basin (HRB) and the Zhanghe River Basin (ZRB) were selected in this study to represent the north arid region and the south humid region in China, respectively. We first evaluated the performance of multi-GCMs derived from Coupled Model Inter-comparison Project Phase 5 (CMIP5) in the two regions based on in-situ measurements and the 40 year European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data. Subsequently, to construct appropriate climate change projection techniques, comparative analysis using two statistical downscaling methods was performed with consideration of the significant north–south meteorological discrepancies. Consequently, specific projections of future climate change for 2021–2050 under three representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5) were completed for the HRB and ZRB, including daily precipitation, maximum air temperature, and minimum air temperature. The results demonstrated that the score-based method with multiple criteria for performance evaluation of multiple GCMs more accurately captured the spatio-temporal characteristics of the regional climate. The two statistical downscaling methods showed respective advantages in arid and humid regions. The statistical downscaling model (SDSM) showed more accurate prediction capacities for air temperature in the arid-climate HRB, whereas model output statistics (MOS) better captured the probability distribution of precipitation in the ZRB, which is characterized by a humid climate. According to the results obtained in this study, the selection of appropriate GCMs and downscaling methods for specific climate zones with different meteorological features significantly impact regional climate change projection. The statistical downscaling models developed and recommended for the north and south of China in this study provide scientific reference for sustainable water resource management subject to climate change.

Highlights

According to the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC), the global mean temperature will continue rising until the end of the 21st century, associatedWater 2020, 12, 3106; doi:10.3390/w12113106 www.mdpi.com/journal/waterWater 2020, 12, 3106 with a significant increase in the frequency and intensity of extreme climate events, which will cause severe socio-economic upheaval and environmental degradation [1,2,3,4]
From the evaluation results of 11 individual statistical values of different climate variables (Tables A5–A12), taking Zhanghe River Basin (ZRB) as the example, we found that the differences in Normalized root mean square error (NRMSE), Cv, Probability density functions (PDFs), and EOF
Most General circulation models (GCMs) overestimated the mean precipitation in the ZRB, and opposite results were obtained for the spatial correlation coefficients and M–K trend analysis, further emphasizing the necessity of multi-criteria evaluation

Summary

Introduction

According to the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC), the global mean temperature will continue rising until the end of the 21st century, associatedWater 2020, 12, 3106; doi:10.3390/w12113106 www.mdpi.com/journal/waterWater 2020, 12, 3106 with a significant increase in the frequency and intensity of extreme climate events, which will cause severe socio-economic upheaval and environmental degradation [1,2,3,4]. Development of projection methods that suitably capture spatio-temporal characteristics of regional climate change has become a key scientific issue to address the impact of global climate change at the regional scale. General circulation models (GCMs) are major tools used to provide large-scale information for the impact assessment of global climate change; their coarse spatial resolution and biased outputs hinder their direct application to climate change prediction at the regional scale [5,6,7]. I.e., regional climate models (RCMs), have been developed based on dynamic formulations using initial and time-dependent lateral boundary conditions of GCMs to generate finer-resolution climate data, which provide more detailed regional information and have been widely used worldwide [8,9,10]. Complexity exists in the computation and mismatch of scales, especially for small-scale watersheds [11,12,13]

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Results

Conclusion